natural product library (TargetMol)
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TargetMol
natural product library
Natural Product Library, supplied by TargetMol, used in various techniques. Bioz Stars score: 95/100, based on 162 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/product+library/Natural+Product+Library/pm42892544-138-1-10
Average 95 stars, based on 162 article reviews
Natural Product Library, supplied by TargetMol, used in various techniques. Bioz Stars score: 95/100, based on 162 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/product+library/Natural+Product+Library/pm42892544-138-1-10
Average 95 stars, based on 162 article reviews
natural product library - by Bioz Stars,
2026-10
95/100 stars
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Screening:Article Title: Integration of regression models and MD simulations for virtual screening of natural compounds: Identification of novel hit scaffolds against SphK1 proteins Article Snippet: .. The bestperforming models were utilised for virtually screening by using the natural Docking Assay:Article Title: Integration of regression models and MD simulations for virtual screening of natural compounds: Identification of novel hit scaffolds against SphK1 proteins Article Snippet: .. The bestperforming models were utilised for virtually screening by using the natural Binding Assay:Article Title: Integration of regression models and MD simulations for virtual screening of natural compounds: Identification of novel hit scaffolds against SphK1 proteins Article Snippet: .. The bestperforming models were utilised for virtually screening by using the natural Drug discovery:Article Title: Atorvastatin Calcium Enhances Ferroptosis in Breast Cancer Cells Through Mechanisms Involving DECR1 Article Snippet: .. The natural Article Title: Predicting small molecule-RNA interactions without RNA tertiary structures. Article Snippet: Small molecules can bind RNAs to regulate their fate and functions, providing promising opportunities for treating human diseases.. However, current tools for predicting small molecule–RNA interactions (SRIs) require prior knowledge of RNA tertiary structures.. Here we present SMRTnet, a deep learning method that uses multimodal data fusion to integrate two large language models with convolutional and graph attention networks to predict SRIs on the basis of RNA secondary structure. |